ArticleEntropy (Basel, Switzerland)2022
Probabilistic Pairwise Model Comparisons Based on Bootstrap Estimators of the Kullback-Leibler Discrepancy.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Bootstrap Approximation of Model Selection Probabilities for Multimodel Inference Frameworks.Entropy (Basel, Switzerland) · 2024Article
- Information and Divergence Measures.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
When choosing between two candidate models, classical hypothesis testing presents two main limitations: first, the models being tested have to be nested, and second, one of the candidate models must subsume the structure of the true data-generating model. Discrepancy measures have been used as an alternative method to select models without the need to rely upon the aforementioned assumptions. In this paper, we utilize a bootstrap approximation of the Kullback-Leibler discrepancy (BD) to estimate the probability that the fitted null model is closer to the underlying generating model than the fitted alternative model. We propose correcting for the bias of the BD estimator either by adding a bootstrap-based correction or by adding the number of parameters in the candidate model. We exemplify the effect of these corrections on the estimator of the discrepancy probability and explore their behavior in different model comparison settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.